Boosting Mixture Models for Semi-supervised Learning View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2001-08-17

AUTHORS

Yves Grandvalet , Florence d’Alché-Buc , Christophe Ambroise

ABSTRACT

This paper introduces MixtBoost, a variant of AdaBoost dedicated to solve problems in which both labeled and unlabeled data are available. We propose several definitions of loss for unlabeled data, from which margins are defined. The resulting boosting schemes implement mixture models as base classifiers. Preliminary experiments are analyzed and the relevance of loss choices is discussed. MixtBoost improves on both mixture models and AdaBoost provided classes are structured, and is otherwise similar to AdaBoost. More... »

PAGES

41-48

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/3-540-44668-0_7

DOI

http://dx.doi.org/10.1007/3-540-44668-0_7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1037922808


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